cwang-nus / prnet

Periodic Residual Learning for Crowd Flow Forecasting (SIGSPATIAL 2022) (official version / arXiv version)

This is the implement of PRNet and its enhanced version using Python 3.8.5, Pytorch 1.7.1, tested on Ubuntu 18.04 with a GeForce RTX 2080 Ti GPU.

Requirements

PRNet uses the following dependencies:

• Pytorch 1.7.1 and its dependencies
• Numpy and Scipy
• CUDA 9.2 or latest version. And cuDNN is highly recommended

Dataset

The datasets we use for model training is detailed in Section 6 of our manuscript. Please download the dataset TaxiBJ-P1.zip for TaxiBJ-P1 from Google Drive.

Put it in folder ./datasets and unzip it.

Model Training

Main input arguments:

• dataset: which dataset to use
• batch_size: training batch size
• base_lr: learning rate
• prev_step: the length of observed segment
• pred_step: the length of target segment
• n_layers: the total number of SCE Blocks
• n_filters: number of filters in each SCE Block
• map_width: width of flow map
• map_height: height of flow map
• s_r: the remaining height/width for SEM module
• s_flag: enable the SEM
• c_flag: enable the clossness segment
• datapath: the path of dataset
• patience: the patience for early stop
• max_epochs: the max number of training epochs
• mode: train or test
• log_dir: directory to save the log and checkpoint

First, please export the PYTHONPATH the model directory.

Then, the following examples are conducted on dataset TaxiBJ-P1:

• Example 1 (PRNet with default settings):

python experiments/prnet/main.py --dataset TaxiBJ-P1 --s_flag --c_flag

• Example 2 (PRNet enhanced version with default settings):

python experiments/prnet/main.py --dataset TaxiBJ-P1 --s_flag --c_flag --x_flag

• Example 3 (PRNet using arbitrary settings):

python experiments/prnet/main.py --dataset TaxiBJ-P1 --max_epochs 200 --n_layers 9 --n_filters 64 --s_flag --c_flag

The trained model will be saved to './log'

Model Test

To test above trained models, you can use the following command to run our code:

python experiments/prnet --dataset TaxiBJ-P1 --mode test --s_flag --c_flag